Each session has an associated “workbook” file which you will use to complete the exercises in the worksheet. The file you need for this session is called session-1.rmd.

If you click on the file it opens the workbook in a tab of a new pane, called the Source pane. It’s called the Source pane because statements writting in the R language are often referred to as ‘R code’, which is shorthand for ‘R source code’. The source pane allows you to write R code and explore your data.

RStudio is a user interface to R, which is a computer language that is primarily designed for data analysis and visualisation. R is a text-based language, so you interact with it by typing and then running statements which have a meaning in the R language.

R Studio makes it easy to run R code and organise your work. For example, you can do simple arithmetic [2 * 221], generate some random numbers with a normal distribution [rnorm(10, 0,1)], and plot some random numbers [hist(rnorm(100, 0,1))].

You should think of R as a robot. The robot is extremely fast, powerful and tireless; but it’s also literal-minded, and won’t think for itself or take the initiative. You need to tell it exactly what to do, by providing very precise instructions.

R Markdown is a file format which allows us to combine R code with narrative text. It allows you to integrate the results of your data analysis into high quality reports, research papers, dissertations or books. Because it’s such a powerful tool, this module provides an early introduction to R Markdown.

R Markdown documents can also be used interactively inside RStudio to run R code.

If you [click on the lifesavr folder in the Files pane of RStudio], you’ll notice that some files have the extension .rmd. These are R Markdown files. The file extension .rmd (or .Rmd) is important, because this is how R Studio knows that the files contain a mixture of R code and narrative text.

RStudio needs to distinguish R code from regular narrative text. This is done by putting the code inside some special characters, creating what’s referred to as a chunk. A chunk is opened using the symbols ```{r}, and closed using the symbols ```. This is what a chunk looks like in RStudio:

A code chunk in the RMarkdown editor

NOTE: The symbols which start and end a chunk are backticks, not single quotes.

Can’t find the backticks on your keyboard?

On windows

On a Mac

A dataset is a set of data relating to a particular topic. Most datasets we will be working with consist of rows and columns, just like a spreadsheet. In R this type of data is stored in a special type of variable called a data.frame. You will also see references to datasets as tibbles. A tibble is just a special type of data.frame, so you can treat the two types of variable as being equivalent.

One data.frame that is built-in to R is called mtcars. This is a dataset about cars that was published in a US magazine called Motor Trend. Let’s display this data in using a new chunk. As we did with the diamonds tibble, if we type mtcars, select the variable name and execute it, we can see the data it contains.

By default this displays only the first ten rows and columns of the data. You can see other rows using the Next, Previous and number buttons below the data. You can see additional columns using the arrow next to the final, right-hand column.

You already know that the diamonds dataset was loaded using library(tidyverse). The gapminder package includes a tibble that contains data about life expectancy, GDP per capita, and population by country. We can load and explore this dataset in a the same way we loaded diamonds dataset. We load the gapminder package, type the name of the tibble (also gapminder) and run it. Again, we can use the navigation buttons to explore the data.

The head() function allows you to explore the first few rows of a dataset. The code mtcars %>% head() prints the first six rows of mtcars. The %>% function is called the ‘pipe’. We’ll explain more about how it works in the next session. For now, all you need to know is that the function on the right hand side of the pipe (head()) is applied to whatever is on the left hand side (in this case, the mtcars dataset).

The glimpse() function is useful for exploring datasets with lots of columns, as it allows you to see all columns at once. Using glimpse() we can have the columns of a dataset run down the page, and data run across.

The World Happiness Report is a survey of the state of global happiness. Running the code happiness %>% glimpse() shows all of the columns. This is like rotating the output you saw earlier anti-clockwise by 90 degrees. The code displays all of the columns as rows, and as many observations from the dataset as will fit on a single line.

Each column in a data.frame has an associated type. The second column of the glimpse output shows you the type of each column. This dataset includes columns with two types:

• dbl is short for ‘double-precision number’, a number with one or more decimal places
• chr — short for ‘character’, a variable which contains text (e.g. an email address)

Other types include:

• fct is short for ‘factor’, a categorical variable (e.g. a specific response to a multiple-choice question)
• int is short for ‘integer’, a variable which contains whole numbers (e.g. a participant id number), and
• ord — short for ‘ordered’; a variant of fct where the categories have a particular order (e.g. responses like ‘Wost’ < ‘Better’ < ‘Best’)

The importance of knowing the type associated with a column will become clear in a later session.

A scatterplot shows the relationship between two variables by plotting their values as points.

This chunk creates a scatter plot by piping the mpg data into the ggplot() function. The plot is built in two steps. The first step, ggplot(aes(cty, hwy)) selects variables for the x and y axes. In this case, miles per gallon (mpg) when cars are driven in the city (cty) will be the x-axis, and mpg when cars are driven on the highway (hwy) will be the y-axis. We can [see the axes by running just this part of the function].

In ggplot, each step is separated by + and goes on a new line. Because R Studio knows this is all part of the same ‘pipeline’, it automatically indents the code.

The second step geom_point() is a function which plots the data as points. If we [run the chunk], we see a scatter plot. A point is plotted for each row, using the values for the cty and hwy variables.

Markdown uses special characters to style text in the same way you might use menus in a word processor to define headings, font styles, lists etc. Your workbook contains examples of the main markdown commands you will need:

• The # at the start of # lifesaveR: Workbook 1 assigns the text lifesaveR: Workbook 1 as a first level heading (the largest size)
• The sentence below that heading is ordinary text
• ## An example plot is a second level heading (next largest). Using ### and #### makes even smaller headings
• **bold text** makes bold text
• *italic text* makes italic text
• The lines beginning 1. under # Exercise 1 create a numbered list, starting at 1

The Knit button combines the markdown and R chunks, ‘knitting’ them together into an output document. It works through your document converting markdown to formatted text, and running each of your R chunks, in the order you have written them. You can see that this is the case by ‘knitting’ the R Markdown workbook for this session.

The following simple approach will give you regular practice writing markdown:

1. Each time you reach a new section in a worksheet, copy the section name (e.g. ‘Exploring and checking data’) into a level 2 heading in your workbook
2. Before starting each exercise, create a level 3 heading for the exercise number [e.g. ### Exercise 5]
3. Copy the exercise instructions below this as a bullet list
4. Add any additional notes that remind you what you have learnt in this section [e.g. Some data is *built-in* to R.]
5. Complete the exercise.

This methodical approach will allow you to complete each workbook step by step. At the end of each session, you will have written a neat, ‘summary’ document which will be a useful revision aid. The same approach works for all R worksheets at the University of Plymouth. If you get into this habit, you will be adding to your ‘reference library’ whenever you complete a worksheet. These summary documents are invaluable when you are more familiar with R but need to quickly remind yourself how to use a particular feature.

The [outline button] in R Studio shows you an outline of your document using the headings you have defined. This makes it easy to find a particular topic and the exercises you have completed to learn about each topic.

The R Markdown Cheat Sheet is a useful quick reference for markdown syntax.